Symbol detection/classification

edit savecancel

It's hard to tell without actually implementing the methods and running experiments.

Having said that, here's what I would do - since the images are black and white, thus showing not texture or color, but only shape, I would describe then using a shape/contour descriptor and use the shape/contour distance function with NN classifier to classify the new symbols by comparing them to a predetermined symbols dataset.

EDIT: As promised, here's a paper surveying shape description techniques: http://vis.uky.edu/~cheung/courses/ee639_fall04/readings/shapeReview.pdf

Using a NN (backpropagation),

You could try the these demo's www.nithinrajs.in/ocr-using-artificial-neural-network-opencv-part-1/ http://www.nithinrajs.in/ocr-using-artificial-neural-network-opencv-part-2-preprocessing/ http://www.nithinrajs.in/ocr-artificial-neural-network-opencv-part-3final-preprocessing/

I successfully got them working.

Additional reference material: http://arxiv.org/ftp/arxiv/papers/1211/1211.4385.pdf

What you need are 2 things

1. Find a good descriptor
2. Find a good classifier

If you can code in C#, I would highly suggest using the Accord.NET Framework since it will have everything you need out of the box. You can easily get something that works juste using the samples that come with the framework. For example, one of the example is able to classify numbers and another one classify the images of different animals.

For the descriptor, there are also a lot of them but since you are in black in white, maybe just give the array of black and white pixel and put that in a KSVM should to the magic.

Hope it helps!

BTW, you can all do these thing in OpenCV but it will need more work!